Avatar Breeder

Avatar Breeder is a genetic algorithm-based procedurally generative satirical artwork constructed by Daniel Upton and Jisun An in Dr. Harrell's graduate course LCC 6312: Design, Technology, and Representation. It is intended to undermine normative categories of identity encountered on bureaucratic forms and in everyday lived experience. In such discrete categorization systems, ethnic identities can be based on geography, nationality, ancestry, family, culture and sub-culture, religion, language, race or any combination of these. In this new ideological thread, how do people identified with mixed ethnicities inhabit the interstitial spaces and margins amidst these complex factors? How do essential categorizations of ethnicity and/or race properly reinforce or obliterate social illusions or personal histories.

Avatar Breeder allows a user to breed avatars together to create new ethnic categories, labeled by users. The user is provided with an initial pool of avatars, each with a labeled ethnicity. The initial pool was seeded with categories from a Georgia Tech admissions form. The user then selects two parents, which genetically combine to create four potential children. Subsequent generations can be created by selecting one of the children and selecting one of four avatars supplied from the initial pool to "breed." All of the different ethnicities begin blending together as the user combines avatars over generations. The user can continue to genetically combine avatars, leaving a family tree tree that ends at the current generation. Throughout the process of breeding a user can trace back through the entire family tree and see which avatars lead to the creation of the newest generation.